基於機器學習演算法之統計相關文件自動分類系統與其在臉書學習型社團線上討論分類

Abstract

本研究旨在利用機器學習的方式，開發一套用於評斷文字內容是否與統計學相關的中文文件分類系統，並將此系統實際應用於Facebook統計課程學習型社團上，針對社團內的貼文與留言進行統計相關與否的二元分類。最後，本研究將比較機器分類與人工分類之間的信度，以探討機器是否能達到與人類相似的分類成效。 實驗結果發現，機器分類模型的準確率最佳達到.917至.950之間，且與人工分類的信度達到.522至.760之間，表示機器除了具備高分類準確度外，也確實有取代人工分類的潛力。

The aim of this study is to develop a Chinese document classification systems for judging whether the content of the text is statistically relevant by means of machine learning algorithms. And the system is applied to the Facebook online discussion group in statistics course, classify posts and comments in the group is statistically relevant or not. Finally, this study will compare the reliability between machine classification and manual classification to explore whether the machine can achieve similar classification with humans. The experimental results show that the accuracy of the machine classification model is between .917 and .950, and the reliability of machine classification and manual classification is between .522 and .760, which means that the machine has high classification accuracy and have the potential to replace manual classification.